Overview

Dataset statistics

Number of variables36
Number of observations1570
Missing cells34827
Missing cells (%)61.6%
Duplicate rows48
Duplicate rows (%)3.1%
Total size in memory441.7 KiB
Average record size in memory288.1 B

Variable types

Numeric21
Categorical13
Unsupported2

Alerts

Dataset has 48 (3.1%) duplicate rowsDuplicates
title has a high cardinality: 257 distinct valuesHigh cardinality
author has a high cardinality: 267 distinct valuesHigh cardinality
location has a high cardinality: 102 distinct valuesHigh cardinality
Journal has a high cardinality: 68 distinct valuesHigh cardinality
Excipients has a high cardinality: 139 distinct valuesHigh cardinality
Additive_1 has a high cardinality: 124 distinct valuesHigh cardinality
title has 628 (40.0%) missing valuesMissing
author has 458 (29.2%) missing valuesMissing
location has 486 (31.0%) missing valuesMissing
Journal has 718 (45.7%) missing valuesMissing
material_0 has 126 (8.0%) missing valuesMissing
material_1 has 618 (39.4%) missing valuesMissing
Excipients has 758 (48.3%) missing valuesMissing
Additive Species has 1098 (69.9%) missing valuesMissing
Additive_1 has 1098 (69.9%) missing valuesMissing
Additive_2 has 1546 (98.5%) missing valuesMissing
Application Rate (%) has 1262 (80.4%) missing valuesMissing
initial moisture content(%) has 509 (32.4%) missing valuesMissing
initial pH has 596 (38.0%) missing valuesMissing
initial TN(%) has 992 (63.2%) missing valuesMissing
initial TC(%) has 1012 (64.5%) missing valuesMissing
initial CN(%) has 453 (28.9%) missing valuesMissing
TN loss (%) has 1007 (64.1%) missing valuesMissing
NH3-N loss (%) has 733 (46.7%) missing valuesMissing
N2O-N loss (%) has 999 (63.6%) missing valuesMissing
TC loss (%) has 1132 (72.1%) missing valuesMissing
CH4-C loss (%) has 1081 (68.9%) missing valuesMissing
CO2-C loss (%) has 1366 (87.0%) missing valuesMissing
Methods has 711 (45.3%) missing valuesMissing
Time Period has 905 (57.6%) missing valuesMissing
堆体大小(m3) has 1422 (90.6%) missing valuesMissing
初始容重(kgL) has 1433 (91.3%) missing valuesMissing
Ventilation has 1156 (73.6%) missing valuesMissing
通风速率(L min-1 kg DW) has 1287 (82.0%) missing valuesMissing
Peak Temperature (℃) has 1445 (92.0%) missing valuesMissing
Average Temperature (℃) has 1497 (95.4%) missing valuesMissing
the ratio of treatment to control-TN has 1373 (87.5%) missing valuesMissing
the ratio of treatment to control-NH3 has 1369 (87.2%) missing valuesMissing
the ratio of treatment to control-N2O has 1471 (93.7%) missing valuesMissing
the ratio of treatment to control-CH4 has 1501 (95.6%) missing valuesMissing
Scale has 581 (37.0%) missing valuesMissing
Time Period is an unsupported type, check if it needs cleaning or further analysisUnsupported
通风速率(L min-1 kg DW) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-09 07:36:39.483930
Analysis finished2024-04-09 07:37:15.752142
Duration36.27 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct434
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.26178
Minimum1
Maximum436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:15.865696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q1100
median171.5
Q3318
95-th percentile422.55
Maximum436
Range435
Interquartile range (IQR)218

Descriptive statistics

Standard deviation127.11867
Coefficient of variation (CV)0.63476249
Kurtosis-1.0989291
Mean200.26178
Median Absolute Deviation (MAD)99.5
Skewness0.32080339
Sum314411
Variance16159.156
MonotonicityNot monotonic
2024-04-09T15:37:16.057943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119 24
 
1.5%
135 24
 
1.5%
173 21
 
1.3%
164 18
 
1.1%
159 18
 
1.1%
435 17
 
1.1%
334 16
 
1.0%
28 16
 
1.0%
39 16
 
1.0%
376 15
 
1.0%
Other values (424) 1385
88.2%
ValueCountFrequency (%)
1 4
0.3%
2 4
0.3%
3 4
0.3%
4 4
0.3%
5 2
0.1%
6 3
0.2%
7 3
0.2%
8 2
0.1%
9 3
0.2%
10 4
0.3%
ValueCountFrequency (%)
436 4
 
0.3%
435 17
1.1%
434 3
 
0.2%
433 3
 
0.2%
432 4
 
0.3%
431 5
 
0.3%
430 9
0.6%
429 2
 
0.1%
428 2
 
0.1%
427 3
 
0.2%

title
Categorical

HIGH CARDINALITY  MISSING 

Distinct257
Distinct (%)27.3%
Missing628
Missing (%)40.0%
Memory size12.4 KiB
Nitrogen, carbon, and dry matter losses during composting of livestock manure with two bulking agents as affected by co-amendments of phosphogypsum and zeolite
 
27
Biochar combined with gypsum reduces both nitrogen and carbon losses during agricultural waste composting and enhances overall compost quality by regulating microbial activities and functions
 
24
Nitrogen loss in chicken litter compost as affected by carbon to nitrogen ratio and turning frequency
 
21
Effect of CN ratio, aeration rate and moisture content on ammonia and greenhouse gas emission during the composting
 
18
不同堆肥条件对堆肥过程中碳素损失及腐殖质形成的影响研究
 
18
Other values (252)
834 

Length

Max length284
Median length191
Mean length98.228238
Min length15

Characters and Unicode

Total characters92531
Distinct characters287
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)10.8%

Sample

1st rowThe co-addition of biochar and manganese ore promotes nitrous oxide reduction but favors methane emission in sewage sludge composting
2nd rowThe co-addition of biochar and manganese ore promotes nitrous oxide reduction but favors methane emission in sewage sludge composting
3rd rowThe co-addition of biochar and manganese ore promotes nitrous oxide reduction but favors methane emission in sewage sludge composting
4th rowThe co-addition of biochar and manganese ore promotes nitrous oxide reduction but favors methane emission in sewage sludge composting
5th rowThe effect of carbonate and biochar on carbon and nitrogen losses during composting

Common Values

ValueCountFrequency (%)
Nitrogen, carbon, and dry matter losses during composting of livestock manure with two bulking agents as affected by co-amendments of phosphogypsum and zeolite 27
 
1.7%
Biochar combined with gypsum reduces both nitrogen and carbon losses during agricultural waste composting and enhances overall compost quality by regulating microbial activities and functions 24
 
1.5%
Nitrogen loss in chicken litter compost as affected by carbon to nitrogen ratio and turning frequency 21
 
1.3%
Effect of CN ratio, aeration rate and moisture content on ammonia and greenhouse gas emission during the composting 18
 
1.1%
不同堆肥条件对堆肥过程中碳素损失及腐殖质形成的影响研究 18
 
1.1%
Effect of aeration rate, CN ratio and moisture content on the stability and maturity of compost 18
 
1.1%
Composting of solids separated from anaerobically digested animal manure: Effect of different bulking agents and mixing ratios on emissions of greenhouse gases and ammonia 17
 
1.1%
碳源调控对污泥堆肥过程氮素损失的影响及其作用机制 17
 
1.1%
Potential of aeration flow rate and bio-char addition to reduce greenhouse gas and ammonia emissions during manure composting 13
 
0.8%
Ammonia emissions and biodegradation of organic carbon during sewage sludge composting with different extra carbon sources 12
 
0.8%
Other values (247) 757
48.2%
(Missing) 628
40.0%

Length

2024-04-09T15:37:16.314287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 946
 
7.4%
and 933
 
7.3%
composting 584
 
4.6%
during 410
 
3.2%
on 322
 
2.5%
manure 296
 
2.3%
the 275
 
2.2%
emissions 236
 
1.8%
nitrogen 213
 
1.7%
greenhouse 181
 
1.4%
Other values (988) 8378
65.6%

Most occurring characters

ValueCountFrequency (%)
11831
 
12.8%
o 7092
 
7.7%
n 6590
 
7.1%
e 6497
 
7.0%
i 6016
 
6.5%
a 5676
 
6.1%
t 5432
 
5.9%
s 5231
 
5.7%
r 4113
 
4.4%
m 3074
 
3.3%
Other values (277) 30979
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70892
76.6%
Space Separator 11889
 
12.8%
Other Letter 5043
 
5.5%
Uppercase Letter 2380
 
2.6%
Other Punctuation 1234
 
1.3%
Decimal Number 673
 
0.7%
Dash Punctuation 272
 
0.3%
Close Punctuation 65
 
0.1%
Open Punctuation 65
 
0.1%
Initial Punctuation 7
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
269
 
5.3%
227
 
4.5%
168
 
3.3%
153
 
3.0%
145
 
2.9%
141
 
2.8%
141
 
2.8%
120
 
2.4%
118
 
2.3%
117
 
2.3%
Other values (189) 3444
68.3%
Lowercase Letter
ValueCountFrequency (%)
o 7092
 
10.0%
n 6590
 
9.3%
e 6497
 
9.2%
i 6016
 
8.5%
a 5676
 
8.0%
t 5432
 
7.7%
s 5231
 
7.4%
r 4113
 
5.8%
m 3074
 
4.3%
d 2805
 
4.0%
Other values (21) 18366
25.9%
Uppercase Letter
ValueCountFrequency (%)
C 349
14.7%
N 303
12.7%
E 280
11.8%
H 123
 
5.2%
A 119
 
5.0%
S 112
 
4.7%
T 105
 
4.4%
G 103
 
4.3%
I 100
 
4.2%
M 96
 
4.0%
Other values (16) 690
29.0%
Decimal Number
ValueCountFrequency (%)
2 135
20.1%
3 114
16.9%
1 96
14.3%
0 87
12.9%
4 68
10.1%
9 52
 
7.7%
5 41
 
6.1%
6 33
 
4.9%
7 30
 
4.5%
8 17
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 640
51.9%
. 474
38.4%
: 75
 
6.1%
28
 
2.3%
/ 17
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 220
80.9%
46
 
16.9%
6
 
2.2%
Other Number
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Space Separator
ValueCountFrequency (%)
11831
99.5%
  58
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 63
96.9%
2
 
3.1%
Open Punctuation
ValueCountFrequency (%)
( 63
96.9%
2
 
3.1%
Initial Punctuation
ValueCountFrequency (%)
6
85.7%
1
 
14.3%
Final Punctuation
ValueCountFrequency (%)
6
85.7%
1
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 73272
79.2%
Common 14216
 
15.4%
Han 5043
 
5.5%

Most frequent character per script

Han
ValueCountFrequency (%)
269
 
5.3%
227
 
4.5%
168
 
3.3%
153
 
3.0%
145
 
2.9%
141
 
2.8%
141
 
2.8%
120
 
2.4%
118
 
2.3%
117
 
2.3%
Other values (189) 3444
68.3%
Latin
ValueCountFrequency (%)
o 7092
 
9.7%
n 6590
 
9.0%
e 6497
 
8.9%
i 6016
 
8.2%
a 5676
 
7.7%
t 5432
 
7.4%
s 5231
 
7.1%
r 4113
 
5.6%
m 3074
 
4.2%
d 2805
 
3.8%
Other values (47) 20746
28.3%
Common
ValueCountFrequency (%)
11831
83.2%
, 640
 
4.5%
. 474
 
3.3%
- 220
 
1.5%
2 135
 
0.9%
3 114
 
0.8%
1 96
 
0.7%
0 87
 
0.6%
: 75
 
0.5%
4 68
 
0.5%
Other values (21) 476
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87310
94.4%
CJK 5043
 
5.5%
None 112
 
0.1%
Punctuation 66
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11831
13.6%
o 7092
 
8.1%
n 6590
 
7.5%
e 6497
 
7.4%
i 6016
 
6.9%
a 5676
 
6.5%
t 5432
 
6.2%
s 5231
 
6.0%
r 4113
 
4.7%
m 3074
 
3.5%
Other values (60) 25758
29.5%
CJK
ValueCountFrequency (%)
269
 
5.3%
227
 
4.5%
168
 
3.3%
153
 
3.0%
145
 
2.9%
141
 
2.8%
141
 
2.8%
120
 
2.4%
118
 
2.3%
117
 
2.3%
Other values (189) 3444
68.3%
None
ValueCountFrequency (%)
  58
51.8%
28
25.0%
á 6
 
5.4%
ó 5
 
4.5%
ö 3
 
2.7%
ü 3
 
2.7%
2
 
1.8%
2
 
1.8%
2
 
1.8%
1
 
0.9%
Other values (2) 2
 
1.8%
Punctuation
ValueCountFrequency (%)
46
69.7%
6
 
9.1%
6
 
9.1%
6
 
9.1%
1
 
1.5%
1
 
1.5%

author
Categorical

HIGH CARDINALITY  MISSING 

Distinct267
Distinct (%)24.0%
Missing458
Missing (%)29.2%
Memory size12.4 KiB
Qu et al., 2020
 
24
Lim et al., 2017
 
24
Ogunwande et al., 2008
 
21
Guo et al., 2012
 
18
Jiang et al., 2011
 
18
Other values (262)
1007 

Length

Max length39
Median length31
Mean length20.044964
Min length8

Characters and Unicode

Total characters22290
Distinct characters70
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)3.0%

Sample

1st rowLimei Ren et al.,2010
2nd rowLimei Ren et al.,2010
3rd rowLimei Ren et al.,2010
4th rowLimei Ren et al.,2010
5th rowKhalil Tubail et al.,2008

Common Values

ValueCountFrequency (%)
Qu et al., 2020 24
 
1.5%
Lim et al., 2017 24
 
1.5%
Ogunwande et al., 2008 21
 
1.3%
Guo et al., 2012 18
 
1.1%
Jiang et al., 2011 18
 
1.1%
Lu, 2007 16
 
1.0%
Leimei Ren et al.,2009 16
 
1.0%
M. Kithome, J. W. Paul et al.,1999 16
 
1.0%
Rincón et al. 2019a 15
 
1.0%
Xiaofeng Lin et al.,2008 13
 
0.8%
Other values (257) 931
59.3%
(Missing) 458
29.2%

Length

2024-04-09T15:37:16.518134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
et 1024
23.2%
al 607
 
13.8%
jiang 90
 
2.0%
al.,2017 78
 
1.8%
2014 71
 
1.6%
2017 71
 
1.6%
2011 66
 
1.5%
2018 58
 
1.3%
li 53
 
1.2%
al.,2016 47
 
1.1%
Other values (266) 2241
50.9%

Most occurring characters

ValueCountFrequency (%)
3304
14.8%
a 1998
 
9.0%
e 1516
 
6.8%
0 1397
 
6.3%
2 1234
 
5.5%
l 1226
 
5.5%
t 1148
 
5.2%
. 1112
 
5.0%
, 977
 
4.4%
1 943
 
4.2%
Other values (60) 7435
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10699
48.0%
Decimal Number 4452
20.0%
Space Separator 3304
 
14.8%
Other Punctuation 2089
 
9.4%
Uppercase Letter 1709
 
7.7%
Dash Punctuation 37
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1998
18.7%
e 1516
14.2%
l 1226
11.5%
t 1148
10.7%
n 902
8.4%
i 747
 
7.0%
u 574
 
5.4%
g 447
 
4.2%
o 383
 
3.6%
h 352
 
3.3%
Other values (20) 1406
13.1%
Uppercase Letter
ValueCountFrequency (%)
L 191
 
11.2%
J 175
 
10.2%
Y 128
 
7.5%
C 106
 
6.2%
S 106
 
6.2%
K 97
 
5.7%
M 95
 
5.6%
H 87
 
5.1%
W 85
 
5.0%
A 78
 
4.6%
Other values (16) 561
32.8%
Decimal Number
ValueCountFrequency (%)
0 1397
31.4%
2 1234
27.7%
1 943
21.2%
7 176
 
4.0%
8 163
 
3.7%
9 153
 
3.4%
6 125
 
2.8%
5 112
 
2.5%
4 100
 
2.2%
3 49
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 1112
53.2%
, 977
46.8%
Space Separator
ValueCountFrequency (%)
3304
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12408
55.7%
Common 9882
44.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1998
16.1%
e 1516
12.2%
l 1226
 
9.9%
t 1148
 
9.3%
n 902
 
7.3%
i 747
 
6.0%
u 574
 
4.6%
g 447
 
3.6%
o 383
 
3.1%
h 352
 
2.8%
Other values (46) 3115
25.1%
Common
ValueCountFrequency (%)
3304
33.4%
0 1397
14.1%
2 1234
 
12.5%
. 1112
 
11.3%
, 977
 
9.9%
1 943
 
9.5%
7 176
 
1.8%
8 163
 
1.6%
9 153
 
1.5%
6 125
 
1.3%
Other values (4) 298
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22243
99.8%
None 47
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3304
14.9%
a 1998
 
9.0%
e 1516
 
6.8%
0 1397
 
6.3%
2 1234
 
5.5%
l 1226
 
5.5%
t 1148
 
5.2%
. 1112
 
5.0%
, 977
 
4.4%
1 943
 
4.2%
Other values (56) 7388
33.2%
None
ValueCountFrequency (%)
ó 20
42.6%
ñ 17
36.2%
á 7
 
14.9%
í 3
 
6.4%

location
Categorical

HIGH CARDINALITY  MISSING 

Distinct102
Distinct (%)9.4%
Missing486
Missing (%)31.0%
Memory size12.4 KiB
China
320 
Europe
72 
Canada
 
53
Asia
 
48
中国北京
 
44
Other values (97)
547 

Length

Max length49
Median length28
Mean length6.1291513
Min length2

Characters and Unicode

Total characters6644
Distinct characters141
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)2.3%

Sample

1st rowChina
2nd rowChina
3rd rowChina
4th rowChina
5th rowUS

Common Values

ValueCountFrequency (%)
China 320
20.4%
Europe 72
 
4.6%
Canada 53
 
3.4%
Asia 48
 
3.1%
中国北京 44
 
2.8%
Japan 36
 
2.3%
中国/北京 31
 
2.0%
US 29
 
1.8%
中国 27
 
1.7%
中国哈尔滨 26
 
1.7%
Other values (92) 398
25.4%
(Missing) 486
31.0%

Length

2024-04-09T15:37:16.688993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
china 320
28.1%
europe 72
 
6.3%
中国北京 59
 
5.2%
canada 57
 
5.0%
asia 48
 
4.2%
japan 36
 
3.2%
中国/北京 35
 
3.1%
us 29
 
2.6%
中国 27
 
2.4%
中国哈尔滨 26
 
2.3%
Other values (97) 428
37.6%

Most occurring characters

ValueCountFrequency (%)
a 816
 
12.3%
n 562
 
8.5%
i 447
 
6.7%
C 377
 
5.7%
h 375
 
5.6%
344
 
5.2%
340
 
5.1%
/ 236
 
3.6%
r 200
 
3.0%
e 193
 
2.9%
Other values (131) 2754
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3448
51.9%
Other Letter 1950
29.3%
Uppercase Letter 900
 
13.5%
Other Punctuation 258
 
3.9%
Space Separator 88
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
344
17.6%
340
17.4%
148
 
7.6%
140
 
7.2%
65
 
3.3%
53
 
2.7%
52
 
2.7%
52
 
2.7%
52
 
2.7%
35
 
1.8%
Other values (86) 669
34.3%
Lowercase Letter
ValueCountFrequency (%)
a 816
23.7%
n 562
16.3%
i 447
13.0%
h 375
10.9%
r 200
 
5.8%
e 193
 
5.6%
o 151
 
4.4%
p 132
 
3.8%
u 108
 
3.1%
d 105
 
3.0%
Other values (10) 359
10.4%
Uppercase Letter
ValueCountFrequency (%)
C 377
41.9%
A 91
 
10.1%
E 88
 
9.8%
S 80
 
8.9%
U 62
 
6.9%
J 48
 
5.3%
D 40
 
4.4%
K 28
 
3.1%
F 17
 
1.9%
N 16
 
1.8%
Other values (9) 53
 
5.9%
Other Punctuation
ValueCountFrequency (%)
/ 236
91.5%
. 19
 
7.4%
' 2
 
0.8%
, 1
 
0.4%
Space Separator
ValueCountFrequency (%)
86
97.7%
  2
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 4348
65.4%
Han 1950
29.3%
Common 346
 
5.2%

Most frequent character per script

Han
ValueCountFrequency (%)
344
17.6%
340
17.4%
148
 
7.6%
140
 
7.2%
65
 
3.3%
53
 
2.7%
52
 
2.7%
52
 
2.7%
52
 
2.7%
35
 
1.8%
Other values (86) 669
34.3%
Latin
ValueCountFrequency (%)
a 816
18.8%
n 562
12.9%
i 447
10.3%
C 377
 
8.7%
h 375
 
8.6%
r 200
 
4.6%
e 193
 
4.4%
o 151
 
3.5%
p 132
 
3.0%
u 108
 
2.5%
Other values (29) 987
22.7%
Common
ValueCountFrequency (%)
/ 236
68.2%
86
 
24.9%
. 19
 
5.5%
  2
 
0.6%
' 2
 
0.6%
, 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4692
70.6%
CJK 1950
29.3%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 816
17.4%
n 562
12.0%
i 447
 
9.5%
C 377
 
8.0%
h 375
 
8.0%
/ 236
 
5.0%
r 200
 
4.3%
e 193
 
4.1%
o 151
 
3.2%
p 132
 
2.8%
Other values (34) 1203
25.6%
CJK
ValueCountFrequency (%)
344
17.6%
340
17.4%
148
 
7.6%
140
 
7.2%
65
 
3.3%
53
 
2.7%
52
 
2.7%
52
 
2.7%
52
 
2.7%
35
 
1.8%
Other values (86) 669
34.3%
None
ValueCountFrequency (%)
  2
100.0%

Journal
Categorical

HIGH CARDINALITY  MISSING 

Distinct68
Distinct (%)8.0%
Missing718
Missing (%)45.7%
Memory size12.4 KiB
Bioresource Technology
208 
Waste Management
67 
Journal of Agro-Environment Science (in Chinese)
51 
Ecological Engineering
 
41
Journal of Environmental Quality
 
37
Other values (63)
448 

Length

Max length75
Median length60
Mean length28.825117
Min length10

Characters and Unicode

Total characters24559
Distinct characters62
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowwaste management
2nd rowwaste management
3rd rowwaste management
4th rowwaste management
5th rowcompost science & utilization

Common Values

ValueCountFrequency (%)
Bioresource Technology 208
 
13.2%
Waste Management 67
 
4.3%
Journal of Agro-Environment Science (in Chinese) 51
 
3.2%
Ecological Engineering 41
 
2.6%
Journal of Environmental Quality 37
 
2.4%
Chemosphere 35
 
2.2%
Journal of Environmental Sciences 26
 
1.7%
Journal of Cleaner Production 23
 
1.5%
Environmental Technology 21
 
1.3%
waste management 21
 
1.3%
Other values (58) 322
20.5%
(Missing) 718
45.7%

Length

2024-04-09T15:37:16.947548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 286
 
10.0%
technology 247
 
8.7%
journal 237
 
8.3%
bioresource 208
 
7.3%
environmental 171
 
6.0%
chinese 162
 
5.7%
science 137
 
4.8%
management 132
 
4.6%
waste 116
 
4.1%
and 76
 
2.7%
Other values (98) 1083
37.9%

Most occurring characters

ValueCountFrequency (%)
e 2678
 
10.9%
n 2603
 
10.6%
o 2289
 
9.3%
2009
 
8.2%
i 1620
 
6.6%
r 1386
 
5.6%
a 1293
 
5.3%
c 1178
 
4.8%
l 1112
 
4.5%
t 968
 
3.9%
Other values (52) 7423
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19733
80.3%
Uppercase Letter 2419
 
9.8%
Space Separator 2013
 
8.2%
Close Punctuation 124
 
0.5%
Open Punctuation 124
 
0.5%
Other Punctuation 85
 
0.3%
Dash Punctuation 57
 
0.2%
Decimal Number 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2678
13.6%
n 2603
13.2%
o 2289
11.6%
i 1620
8.2%
r 1386
 
7.0%
a 1293
 
6.6%
c 1178
 
6.0%
l 1112
 
5.6%
t 968
 
4.9%
s 833
 
4.2%
Other values (14) 3773
19.1%
Uppercase Letter
ValueCountFrequency (%)
E 424
17.5%
T 276
11.4%
C 263
10.9%
B 260
10.7%
J 253
10.5%
S 241
10.0%
A 148
 
6.1%
M 138
 
5.7%
W 97
 
4.0%
P 85
 
3.5%
Other values (14) 234
9.7%
Other Punctuation
ValueCountFrequency (%)
& 38
44.7%
. 23
27.1%
, 20
23.5%
2
 
2.4%
" 2
 
2.4%
Decimal Number
ValueCountFrequency (%)
2 1
25.0%
0 1
25.0%
1 1
25.0%
5 1
25.0%
Space Separator
ValueCountFrequency (%)
2009
99.8%
  4
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 124
100.0%
Open Punctuation
ValueCountFrequency (%)
( 124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22152
90.2%
Common 2407
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2678
12.1%
n 2603
11.8%
o 2289
 
10.3%
i 1620
 
7.3%
r 1386
 
6.3%
a 1293
 
5.8%
c 1178
 
5.3%
l 1112
 
5.0%
t 968
 
4.4%
s 833
 
3.8%
Other values (38) 6192
28.0%
Common
ValueCountFrequency (%)
2009
83.5%
) 124
 
5.2%
( 124
 
5.2%
- 57
 
2.4%
& 38
 
1.6%
. 23
 
1.0%
, 20
 
0.8%
  4
 
0.2%
2
 
0.1%
" 2
 
0.1%
Other values (4) 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24553
> 99.9%
None 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2678
 
10.9%
n 2603
 
10.6%
o 2289
 
9.3%
2009
 
8.2%
i 1620
 
6.6%
r 1386
 
5.6%
a 1293
 
5.3%
c 1178
 
4.8%
l 1112
 
4.5%
t 968
 
3.9%
Other values (50) 7417
30.2%
None
ValueCountFrequency (%)
  4
66.7%
2
33.3%

material_0
Categorical

Distinct6
Distinct (%)0.4%
Missing126
Missing (%)8.0%
Memory size12.4 KiB
Manure
952 
Sewage sludge
240 
Food waste
115 
Lignin
 
92
Digestate
 
43

Length

Max length13
Median length6
Mean length7.5699446
Min length5

Characters and Unicode

Total characters10931
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManure
2nd rowManure
3rd rowManure
4th rowManure
5th rowManure

Common Values

ValueCountFrequency (%)
Manure 952
60.6%
Sewage sludge 240
 
15.3%
Food waste 115
 
7.3%
Lignin 92
 
5.9%
Digestate 43
 
2.7%
Other 2
 
0.1%
(Missing) 126
 
8.0%

Length

2024-04-09T15:37:17.137647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T15:37:17.335422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
manure 952
52.9%
sewage 240
 
13.3%
sludge 240
 
13.3%
food 115
 
6.4%
waste 115
 
6.4%
lignin 92
 
5.1%
digestate 43
 
2.4%
other 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 1875
17.2%
a 1350
12.4%
u 1192
10.9%
n 1136
10.4%
r 954
8.7%
M 952
8.7%
g 615
 
5.6%
s 398
 
3.6%
w 355
 
3.2%
355
 
3.2%
Other values (11) 1749
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9132
83.5%
Uppercase Letter 1444
 
13.2%
Space Separator 355
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1875
20.5%
a 1350
14.8%
u 1192
13.1%
n 1136
12.4%
r 954
10.4%
g 615
 
6.7%
s 398
 
4.4%
w 355
 
3.9%
d 355
 
3.9%
l 240
 
2.6%
Other values (4) 662
 
7.2%
Uppercase Letter
ValueCountFrequency (%)
M 952
65.9%
S 240
 
16.6%
F 115
 
8.0%
L 92
 
6.4%
D 43
 
3.0%
O 2
 
0.1%
Space Separator
ValueCountFrequency (%)
355
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10576
96.8%
Common 355
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1875
17.7%
a 1350
12.8%
u 1192
11.3%
n 1136
10.7%
r 954
9.0%
M 952
9.0%
g 615
 
5.8%
s 398
 
3.8%
w 355
 
3.4%
d 355
 
3.4%
Other values (10) 1394
13.2%
Common
ValueCountFrequency (%)
355
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10931
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1875
17.2%
a 1350
12.4%
u 1192
10.9%
n 1136
10.4%
r 954
8.7%
M 952
8.7%
g 615
 
5.6%
s 398
 
3.6%
w 355
 
3.2%
355
 
3.2%
Other values (11) 1749
16.0%

material_1
Categorical

Distinct7
Distinct (%)0.7%
Missing618
Missing (%)39.4%
Memory size12.4 KiB
Swine manure
440 
Poultry manure
235 
Cow manure
212 
Manure
57 
Horse manure
 
5
Other values (2)
 
3

Length

Max length14
Median length13
Mean length11.685924
Min length6

Characters and Unicode

Total characters11125
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSwine manure
2nd rowSwine manure
3rd rowSwine manure
4th rowSwine manure
5th rowCow manure

Common Values

ValueCountFrequency (%)
Swine manure 440
28.0%
Poultry manure 235
 
15.0%
Cow manure 212
 
13.5%
Manure 57
 
3.6%
Horse manure 5
 
0.3%
Human manure 2
 
0.1%
Manure 1
 
0.1%
(Missing) 618
39.4%

Length

2024-04-09T15:37:17.617475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T15:37:17.810877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
manure 952
51.6%
swine 440
23.8%
poultry 235
 
12.7%
cow 212
 
11.5%
horse 5
 
0.3%
human 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 1397
12.6%
n 1394
12.5%
r 1192
10.7%
u 1189
10.7%
a 954
8.6%
897
8.1%
m 896
8.1%
w 652
 
5.9%
o 452
 
4.1%
S 440
 
4.0%
Other values (9) 1662
14.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9276
83.4%
Uppercase Letter 952
 
8.6%
Space Separator 897
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1397
15.1%
n 1394
15.0%
r 1192
12.9%
u 1189
12.8%
a 954
10.3%
m 896
9.7%
w 652
7.0%
o 452
 
4.9%
i 440
 
4.7%
l 235
 
2.5%
Other values (3) 475
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
S 440
46.2%
P 235
24.7%
C 212
22.3%
M 58
 
6.1%
H 7
 
0.7%
Space Separator
ValueCountFrequency (%)
897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10228
91.9%
Common 897
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1397
13.7%
n 1394
13.6%
r 1192
11.7%
u 1189
11.6%
a 954
9.3%
m 896
8.8%
w 652
6.4%
o 452
 
4.4%
S 440
 
4.3%
i 440
 
4.3%
Other values (8) 1222
11.9%
Common
ValueCountFrequency (%)
897
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1397
12.6%
n 1394
12.5%
r 1192
10.7%
u 1189
10.7%
a 954
8.6%
897
8.1%
m 896
8.1%
w 652
 
5.9%
o 452
 
4.1%
S 440
 
4.0%
Other values (9) 1662
14.9%

Excipients
Categorical

HIGH CARDINALITY  MISSING 

Distinct139
Distinct (%)17.1%
Missing758
Missing (%)48.3%
Memory size12.4 KiB
玉米秸秆
96 
小麦秸秆
 
46
Wheat straw
 
37
Sawdust
 
36
锯末
 
30
Other values (134)
567 

Length

Max length38
Median length32
Mean length8.4371921
Min length2

Characters and Unicode

Total characters6851
Distinct characters130
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)6.8%

Sample

1st rowCorn straw
2nd rowCorn straw
3rd rowCorn straw
4th rowCorn straw
5th rowCereal chaff

Common Values

ValueCountFrequency (%)
玉米秸秆 96
 
6.1%
小麦秸秆 46
 
2.9%
Wheat straw 37
 
2.4%
Sawdust 36
 
2.3%
锯末 30
 
1.9%
Cornstalk 27
 
1.7%
木屑 27
 
1.7%
稻草 27
 
1.7%
Plant straw 24
 
1.5%
蘑菇渣 21
 
1.3%
Other values (129) 441
28.1%
(Missing) 758
48.3%

Length

2024-04-09T15:37:18.008334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
straw 126
 
10.1%
玉米秸秆 96
 
7.7%
sawdust 63
 
5.0%
corn 54
 
4.3%
小麦秸秆 52
 
4.2%
45
 
3.6%
wheat 40
 
3.2%
manure 39
 
3.1%
stalks 37
 
3.0%
锯末 30
 
2.4%
Other values (142) 671
53.6%

Most occurring characters

ValueCountFrequency (%)
a 550
 
8.0%
491
 
7.2%
t 472
 
6.9%
s 449
 
6.6%
r 366
 
5.3%
e 353
 
5.2%
w 251
 
3.7%
o 227
 
3.3%
223
 
3.3%
223
 
3.3%
Other values (120) 3246
47.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4054
59.2%
Other Letter 1666
24.3%
Space Separator 491
 
7.2%
Uppercase Letter 430
 
6.3%
Math Symbol 200
 
2.9%
Other Punctuation 9
 
0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
223
 
13.4%
223
 
13.4%
113
 
6.8%
103
 
6.2%
80
 
4.8%
71
 
4.3%
70
 
4.2%
59
 
3.5%
50
 
3.0%
44
 
2.6%
Other values (75) 630
37.8%
Lowercase Letter
ValueCountFrequency (%)
a 550
13.6%
t 472
11.6%
s 449
11.1%
r 366
9.0%
e 353
8.7%
w 251
 
6.2%
o 227
 
5.6%
n 216
 
5.3%
l 197
 
4.9%
u 190
 
4.7%
Other values (13) 783
19.3%
Uppercase Letter
ValueCountFrequency (%)
S 119
27.7%
C 98
22.8%
P 51
11.9%
W 38
 
8.8%
M 34
 
7.9%
R 18
 
4.2%
B 17
 
4.0%
D 14
 
3.3%
G 9
 
2.1%
F 9
 
2.1%
Other values (6) 23
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 4
44.4%
. 4
44.4%
1
 
11.1%
Space Separator
ValueCountFrequency (%)
491
100.0%
Math Symbol
ValueCountFrequency (%)
+ 200
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4484
65.5%
Han 1666
 
24.3%
Common 701
 
10.2%

Most frequent character per script

Han
ValueCountFrequency (%)
223
 
13.4%
223
 
13.4%
113
 
6.8%
103
 
6.2%
80
 
4.8%
71
 
4.3%
70
 
4.2%
59
 
3.5%
50
 
3.0%
44
 
2.6%
Other values (75) 630
37.8%
Latin
ValueCountFrequency (%)
a 550
12.3%
t 472
 
10.5%
s 449
 
10.0%
r 366
 
8.2%
e 353
 
7.9%
w 251
 
5.6%
o 227
 
5.1%
n 216
 
4.8%
l 197
 
4.4%
u 190
 
4.2%
Other values (29) 1213
27.1%
Common
ValueCountFrequency (%)
491
70.0%
+ 200
28.5%
, 4
 
0.6%
. 4
 
0.6%
1
 
0.1%
4 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5184
75.7%
CJK 1666
 
24.3%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 550
 
10.6%
491
 
9.5%
t 472
 
9.1%
s 449
 
8.7%
r 366
 
7.1%
e 353
 
6.8%
w 251
 
4.8%
o 227
 
4.4%
n 216
 
4.2%
+ 200
 
3.9%
Other values (34) 1609
31.0%
CJK
ValueCountFrequency (%)
223
 
13.4%
223
 
13.4%
113
 
6.8%
103
 
6.2%
80
 
4.8%
71
 
4.3%
70
 
4.2%
59
 
3.5%
50
 
3.0%
44
 
2.6%
Other values (75) 630
37.8%
None
ValueCountFrequency (%)
1
100.0%

Additive Species
Categorical

Distinct4
Distinct (%)0.8%
Missing1098
Missing (%)69.9%
Memory size12.4 KiB
Physical
286 
Chemical
132 
Biological
39 
Mixture
 
15

Length

Max length10
Median length8
Mean length8.1334746
Min length7

Characters and Unicode

Total characters3839
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChemical
2nd rowChemical
3rd rowChemical
4th rowPhysical
5th rowPhysical

Common Values

ValueCountFrequency (%)
Physical 286
 
18.2%
Chemical 132
 
8.4%
Biological 39
 
2.5%
Mixture 15
 
1.0%
(Missing) 1098
69.9%

Length

2024-04-09T15:37:18.199385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T15:37:18.366407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
physical 286
60.6%
chemical 132
28.0%
biological 39
 
8.3%
mixture 15
 
3.2%

Most occurring characters

ValueCountFrequency (%)
i 511
13.3%
l 496
12.9%
c 457
11.9%
a 457
11.9%
h 418
10.9%
P 286
7.4%
y 286
7.4%
s 286
7.4%
e 147
 
3.8%
C 132
 
3.4%
Other values (9) 363
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3367
87.7%
Uppercase Letter 472
 
12.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 511
15.2%
l 496
14.7%
c 457
13.6%
a 457
13.6%
h 418
12.4%
y 286
8.5%
s 286
8.5%
e 147
 
4.4%
m 132
 
3.9%
o 78
 
2.3%
Other values (5) 99
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
P 286
60.6%
C 132
28.0%
B 39
 
8.3%
M 15
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 3839
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 511
13.3%
l 496
12.9%
c 457
11.9%
a 457
11.9%
h 418
10.9%
P 286
7.4%
y 286
7.4%
s 286
7.4%
e 147
 
3.8%
C 132
 
3.4%
Other values (9) 363
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3839
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 511
13.3%
l 496
12.9%
c 457
11.9%
a 457
11.9%
h 418
10.9%
P 286
7.4%
y 286
7.4%
s 286
7.4%
e 147
 
3.8%
C 132
 
3.4%
Other values (9) 363
9.5%

Additive_1
Categorical

HIGH CARDINALITY  MISSING 

Distinct124
Distinct (%)26.3%
Missing1098
Missing (%)69.9%
Memory size12.4 KiB
biochar
56 
PO43- and Mg2+ salts
46 
superphosphate
32 
zeolite
31 
Biochar
 
26
Other values (119)
281 

Length

Max length119
Median length32
Mean length13.436441
Min length1

Characters and Unicode

Total characters6342
Distinct characters60
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)14.0%

Sample

1st rowPO43- and Mg2+ salts
2nd rowPO43- and Mg2+ salts
3rd rowPO43- and Mg2+ salts
4th rowgypsum
5th rowgypsum

Common Values

ValueCountFrequency (%)
biochar 56
 
3.6%
PO43- and Mg2+ salts 46
 
2.9%
superphosphate 32
 
2.0%
zeolite 31
 
2.0%
Biochar 26
 
1.7%
phosphogypsum 15
 
1.0%
gypsum 12
 
0.8%
Biochar 9
 
0.6%
clay 8
 
0.5%
microbiological agent 8
 
0.5%
Other values (114) 229
 
14.6%
(Missing) 1098
69.9%

Length

2024-04-09T15:37:18.543628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
biochar 108
 
12.9%
and 67
 
8.0%
mg2 46
 
5.5%
salts 46
 
5.5%
po43 46
 
5.5%
superphosphate 40
 
4.8%
zeolite 34
 
4.0%
phosphogypsum 21
 
2.5%
gypsum 18
 
2.1%
clay 13
 
1.5%
Other values (140) 401
47.7%

Most occurring characters

ValueCountFrequency (%)
a 564
 
8.9%
o 422
 
6.7%
406
 
6.4%
i 405
 
6.4%
e 392
 
6.2%
s 363
 
5.7%
t 299
 
4.7%
r 294
 
4.6%
h 289
 
4.6%
p 260
 
4.1%
Other values (50) 2648
41.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4893
77.2%
Uppercase Letter 558
 
8.8%
Space Separator 420
 
6.6%
Decimal Number 293
 
4.6%
Dash Punctuation 57
 
0.9%
Math Symbol 56
 
0.9%
Open Punctuation 23
 
0.4%
Close Punctuation 23
 
0.4%
Other Punctuation 17
 
0.3%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 564
11.5%
o 422
 
8.6%
i 405
 
8.3%
e 392
 
8.0%
s 363
 
7.4%
t 299
 
6.1%
r 294
 
6.0%
h 289
 
5.9%
p 260
 
5.3%
c 229
 
4.7%
Other values (15) 1376
28.1%
Uppercase Letter
ValueCountFrequency (%)
O 121
21.7%
P 79
14.2%
M 62
11.1%
H 54
9.7%
B 54
9.7%
C 41
 
7.3%
S 32
 
5.7%
N 30
 
5.4%
T 22
 
3.9%
A 18
 
3.2%
Other values (8) 45
 
8.1%
Decimal Number
ValueCountFrequency (%)
2 105
35.8%
4 96
32.8%
3 67
22.9%
1 9
 
3.1%
0 8
 
2.7%
5 7
 
2.4%
9 1
 
0.3%
Space Separator
ValueCountFrequency (%)
406
96.7%
  10
 
2.4%
4
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 16
94.1%
. 1
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%
Math Symbol
ValueCountFrequency (%)
+ 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5451
86.0%
Common 891
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 564
 
10.3%
o 422
 
7.7%
i 405
 
7.4%
e 392
 
7.2%
s 363
 
6.7%
t 299
 
5.5%
r 294
 
5.4%
h 289
 
5.3%
p 260
 
4.8%
c 229
 
4.2%
Other values (33) 1934
35.5%
Common
ValueCountFrequency (%)
406
45.6%
2 105
 
11.8%
4 96
 
10.8%
3 67
 
7.5%
- 57
 
6.4%
+ 56
 
6.3%
( 23
 
2.6%
) 23
 
2.6%
, 16
 
1.8%
  10
 
1.1%
Other values (7) 32
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6328
99.8%
None 10
 
0.2%
Punctuation 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 564
 
8.9%
o 422
 
6.7%
406
 
6.4%
i 405
 
6.4%
e 392
 
6.2%
s 363
 
5.7%
t 299
 
4.7%
r 294
 
4.6%
h 289
 
4.6%
p 260
 
4.1%
Other values (48) 2634
41.6%
None
ValueCountFrequency (%)
  10
100.0%
Punctuation
ValueCountFrequency (%)
4
100.0%

Additive_2
Categorical

Distinct10
Distinct (%)41.7%
Missing1546
Missing (%)98.5%
Memory size12.4 KiB
zeolite
DCD
Gypsum
Calcium-bontonite
Urea
Other values (5)

Length

Max length19
Median length16
Mean length8.625
Min length3

Characters and Unicode

Total characters207
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)20.8%

Sample

1st rowactivated charcoal
2nd row Manganese ore
3rd row Ferrous Sulfate
4th rowBean dreg
5th rowGypsum

Common Values

ValueCountFrequency (%)
zeolite 6
 
0.4%
DCD 4
 
0.3%
Gypsum 4
 
0.3%
Calcium-bontonite 3
 
0.2%
Urea 2
 
0.1%
activated charcoal 1
 
0.1%
Ferrous Sulfate 1
 
0.1%
sheep manure 1
 
0.1%
Manganese ore 1
 
0.1%
Bean dreg 1
 
0.1%
(Missing) 1546
98.5%

Length

2024-04-09T15:37:18.726182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T15:37:18.907234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
zeolite 6
20.7%
dcd 4
13.8%
gypsum 4
13.8%
calcium-bontonite 3
10.3%
urea 2
 
6.9%
activated 1
 
3.4%
charcoal 1
 
3.4%
ferrous 1
 
3.4%
sulfate 1
 
3.4%
sheep 1
 
3.4%
Other values (5) 5
17.2%

Most occurring characters

ValueCountFrequency (%)
e 28
 
13.5%
o 15
 
7.2%
t 15
 
7.2%
a 14
 
6.8%
i 13
 
6.3%
l 11
 
5.3%
n 10
 
4.8%
u 10
 
4.8%
D 8
 
3.9%
8
 
3.9%
Other values (21) 75
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 171
82.6%
Uppercase Letter 25
 
12.1%
Space Separator 8
 
3.9%
Dash Punctuation 3
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28
16.4%
o 15
 
8.8%
t 15
 
8.8%
a 14
 
8.2%
i 13
 
7.6%
l 11
 
6.4%
n 10
 
5.8%
u 10
 
5.8%
r 8
 
4.7%
m 8
 
4.7%
Other values (11) 39
22.8%
Uppercase Letter
ValueCountFrequency (%)
D 8
32.0%
C 7
28.0%
G 4
16.0%
U 2
 
8.0%
F 1
 
4.0%
S 1
 
4.0%
M 1
 
4.0%
B 1
 
4.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 196
94.7%
Common 11
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28
14.3%
o 15
 
7.7%
t 15
 
7.7%
a 14
 
7.1%
i 13
 
6.6%
l 11
 
5.6%
n 10
 
5.1%
u 10
 
5.1%
D 8
 
4.1%
r 8
 
4.1%
Other values (19) 64
32.7%
Common
ValueCountFrequency (%)
8
72.7%
- 3
 
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 28
 
13.5%
o 15
 
7.2%
t 15
 
7.2%
a 14
 
6.8%
i 13
 
6.3%
l 11
 
5.3%
n 10
 
4.8%
u 10
 
4.8%
D 8
 
3.9%
8
 
3.9%
Other values (21) 75
36.2%

Application Rate (%)
Real number (ℝ)

Distinct107
Distinct (%)34.7%
Missing1262
Missing (%)80.4%
Infinite0
Infinite (%)0.0%
Mean13.972546
Minimum0.002
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:19.085585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile1
Q15.15
median10
Q320
95-th percentile33
Maximum54
Range53.998
Interquartile range (IQR)14.85

Descriptive statistics

Standard deviation10.7479
Coefficient of variation (CV)0.76921555
Kurtosis1.7477216
Mean13.972546
Median Absolute Deviation (MAD)5.3
Skewness1.2370117
Sum4303.5443
Variance115.51735
MonotonicityNot monotonic
2024-04-09T15:37:19.243246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 46
 
2.9%
5 25
 
1.6%
25 16
 
1.0%
20 16
 
1.0%
15 14
 
0.9%
23 12
 
0.8%
1 10
 
0.6%
6 8
 
0.5%
14 7
 
0.4%
33 7
 
0.4%
Other values (97) 147
 
9.4%
(Missing) 1262
80.4%
ValueCountFrequency (%)
0.002 1
 
0.1%
0.25 3
 
0.2%
0.3465 1
 
0.1%
0.38475 1
 
0.1%
0.4 3
 
0.2%
0.42075 1
 
0.1%
0.6 1
 
0.1%
0.88 1
 
0.1%
1 10
0.6%
1.5 1
 
0.1%
ValueCountFrequency (%)
54 1
 
0.1%
52.6 1
 
0.1%
51 1
 
0.1%
50 3
0.2%
47 1
 
0.1%
42 1
 
0.1%
40 1
 
0.1%
38.9 2
0.1%
38 1
 
0.1%
37.8 2
0.1%
Distinct322
Distinct (%)30.3%
Missing509
Missing (%)32.4%
Infinite0
Infinite (%)0.0%
Mean65.478364
Minimum40
Maximum89.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:19.400919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile52.8
Q160
median65
Q370
95-th percentile81.08
Maximum89.8
Range49.8
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.1561721
Coefficient of variation (CV)0.12456286
Kurtosis0.50852021
Mean65.478364
Median Absolute Deviation (MAD)5
Skewness0.30310652
Sum69472.544
Variance66.523144
MonotonicityNot monotonic
2024-04-09T15:37:19.559229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 124
 
7.9%
65 117
 
7.5%
70 36
 
2.3%
55 24
 
1.5%
63 19
 
1.2%
72.1 14
 
0.9%
61 13
 
0.8%
75 12
 
0.8%
66 12
 
0.8%
64 12
 
0.8%
Other values (312) 678
43.2%
(Missing) 509
32.4%
ValueCountFrequency (%)
40 2
 
0.1%
43.6 1
 
0.1%
43.61 1
 
0.1%
44 1
 
0.1%
45 5
0.3%
45.32 1
 
0.1%
46.3 1
 
0.1%
46.7 1
 
0.1%
46.9 2
 
0.1%
47.5 3
0.2%
ValueCountFrequency (%)
89.8 1
 
0.1%
88.5 7
0.4%
86 1
 
0.1%
85.5 1
 
0.1%
85.33 2
 
0.1%
85 3
0.2%
84.89 1
 
0.1%
84.7 1
 
0.1%
84.66 2
 
0.1%
84.3 5
0.3%

initial pH
Real number (ℝ)

Distinct278
Distinct (%)28.5%
Missing596
Missing (%)38.0%
Infinite0
Infinite (%)0.0%
Mean7.4673292
Minimum3.51
Maximum10.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:19.718665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.51
5-th percentile5.693
Q16.9
median7.6
Q38.17
95-th percentile8.8
Maximum10.7
Range7.19
Interquartile range (IQR)1.27

Descriptive statistics

Standard deviation0.97858746
Coefficient of variation (CV)0.13104919
Kurtosis0.48184693
Mean7.4673292
Median Absolute Deviation (MAD)0.61
Skewness-0.52683973
Sum7273.1786
Variance0.95763342
MonotonicityNot monotonic
2024-04-09T15:37:19.885231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.6 46
 
2.9%
7.6 29
 
1.8%
7.8 28
 
1.8%
6.5 22
 
1.4%
8.3 21
 
1.3%
8.2 21
 
1.3%
7.1 20
 
1.3%
6.9 19
 
1.2%
8.4 19
 
1.2%
8 19
 
1.2%
Other values (268) 730
46.5%
(Missing) 596
38.0%
ValueCountFrequency (%)
3.51 1
 
0.1%
4.28 2
0.1%
4.42231 1
 
0.1%
4.43 2
0.1%
4.45 1
 
0.1%
4.8 1
 
0.1%
4.81275 1
 
0.1%
4.9 1
 
0.1%
4.92 1
 
0.1%
5 3
0.2%
ValueCountFrequency (%)
10.7 1
0.1%
10.32 2
0.1%
10 1
0.1%
9.8 2
0.1%
9.6 1
0.1%
9.4 1
0.1%
9.35 1
0.1%
9.33 1
0.1%
9.2 2
0.1%
9.19 1
0.1%

initial TN(%)
Real number (ℝ)

Distinct280
Distinct (%)48.4%
Missing992
Missing (%)63.2%
Infinite0
Infinite (%)0.0%
Mean2.5465543
Minimum0.37
Maximum14.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:20.028767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile0.9185
Q11.46
median2.02
Q32.7375
95-th percentile7.5
Maximum14.56
Range14.19
Interquartile range (IQR)1.2775

Descriptive statistics

Standard deviation1.9951525
Coefficient of variation (CV)0.78347143
Kurtosis11.613934
Mean2.5465543
Median Absolute Deviation (MAD)0.62
Skewness3.0938075
Sum1471.9084
Variance3.9806337
MonotonicityNot monotonic
2024-04-09T15:37:20.174325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 19
 
1.2%
3.103822807 18
 
1.1%
1.4 14
 
0.9%
2.4 13
 
0.8%
2.1 13
 
0.8%
1.7 10
 
0.6%
1.8 10
 
0.6%
1.79 9
 
0.6%
1.78 8
 
0.5%
2.04 7
 
0.4%
Other values (270) 457
29.1%
(Missing) 992
63.2%
ValueCountFrequency (%)
0.37 1
 
0.1%
0.68 2
0.1%
0.69 3
0.2%
0.7 2
0.1%
0.723 1
 
0.1%
0.77 1
 
0.1%
0.782 1
 
0.1%
0.794 1
 
0.1%
0.8 2
0.1%
0.807 1
 
0.1%
ValueCountFrequency (%)
14.56 1
0.1%
14.2 2
0.1%
13.4 1
0.1%
13.1 1
0.1%
11.58 1
0.1%
11.12 1
0.1%
10.4 1
0.1%
10.27 1
0.1%
10 1
0.1%
9.79 1
0.1%

initial TC(%)
Real number (ℝ)

Distinct332
Distinct (%)59.5%
Missing1012
Missing (%)64.5%
Infinite0
Infinite (%)0.0%
Mean50.863042
Minimum1.45
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:20.340538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile24.8985
Q135.9
median40.7
Q349
95-th percentile135.15
Maximum197
Range195.55
Interquartile range (IQR)13.1

Descriptive statistics

Standard deviation33.106559
Coefficient of variation (CV)0.65089617
Kurtosis6.9225999
Mean50.863042
Median Absolute Deviation (MAD)6.3
Skewness2.6280395
Sum28381.577
Variance1096.0442
MonotonicityNot monotonic
2024-04-09T15:37:20.500031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.57221742 18
 
1.1%
38 9
 
0.6%
72.33 8
 
0.5%
37.48 7
 
0.4%
37.3 7
 
0.4%
40 7
 
0.4%
36.9 7
 
0.4%
78.27 7
 
0.4%
48.99 7
 
0.4%
35.2 7
 
0.4%
Other values (322) 474
30.2%
(Missing) 1012
64.5%
ValueCountFrequency (%)
1.45 3
0.2%
5.8 1
 
0.1%
5.85 1
 
0.1%
6 1
 
0.1%
6.35 1
 
0.1%
19.14 1
 
0.1%
19.152 1
 
0.1%
19.3 1
 
0.1%
20.2 1
 
0.1%
21.8 1
 
0.1%
ValueCountFrequency (%)
197 1
0.1%
196.75 2
0.1%
193.8 1
0.1%
192.61 1
0.1%
185 1
0.1%
180.67 1
0.1%
180.05 1
0.1%
178.23 1
0.1%
177.9 1
0.1%
174.91 1
0.1%

initial CN(%)
Real number (ℝ)

Distinct477
Distinct (%)42.7%
Missing453
Missing (%)28.9%
Infinite0
Infinite (%)0.0%
Mean21.51954
Minimum1.1
Maximum53.73494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:20.669031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile9.715
Q115.508257
median20
Q325.21
95-th percentile37.516812
Maximum53.73494
Range52.63494
Interquartile range (IQR)9.7017431

Descriptive statistics

Standard deviation8.7820421
Coefficient of variation (CV)0.40809619
Kurtosis1.4460812
Mean21.51954
Median Absolute Deviation (MAD)5
Skewness0.95790181
Sum24037.326
Variance77.124263
MonotonicityNot monotonic
2024-04-09T15:37:20.827527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 76
 
4.8%
30 59
 
3.8%
20 42
 
2.7%
15 32
 
2.0%
18 24
 
1.5%
21 16
 
1.0%
17.8 14
 
0.9%
19 13
 
0.8%
18.4 12
 
0.8%
29 12
 
0.8%
Other values (467) 817
52.0%
(Missing) 453
28.9%
ValueCountFrequency (%)
1.1 1
 
0.1%
2.536745928 1
 
0.1%
2.901592789 1
 
0.1%
4.43 3
 
0.2%
4.89 1
 
0.1%
4.916910084 1
 
0.1%
5.22 1
 
0.1%
5.4 1
 
0.1%
6.1 9
0.6%
6.15 1
 
0.1%
ValueCountFrequency (%)
53.73493976 1
0.1%
53.69565217 1
0.1%
53.37662338 1
0.1%
53.00827305 1
0.1%
52.88659794 2
0.1%
52.0212766 1
0.1%
51.70992366 1
0.1%
51.54255319 1
0.1%
51.5 1
0.1%
51.20879121 1
0.1%

TN loss (%)
Real number (ℝ)

Distinct395
Distinct (%)70.2%
Missing1007
Missing (%)64.1%
Infinite0
Infinite (%)0.0%
Mean28.950867
Minimum0.2
Maximum90.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:20.987859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2.73
Q115.65
median26.530612
Q339.7
95-th percentile63
Maximum90.5
Range90.3
Interquartile range (IQR)24.05

Descriptive statistics

Standard deviation18.936026
Coefficient of variation (CV)0.65407459
Kurtosis1.1344181
Mean28.950867
Median Absolute Deviation (MAD)11.330612
Skewness1.0011898
Sum16299.338
Variance358.5731
MonotonicityNot monotonic
2024-04-09T15:37:21.119545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 6
 
0.4%
22 6
 
0.4%
15.5 5
 
0.3%
37.5 5
 
0.3%
48 5
 
0.3%
16.99 4
 
0.3%
17.6 4
 
0.3%
23 4
 
0.3%
58 4
 
0.3%
18.8 4
 
0.3%
Other values (385) 516
32.9%
(Missing) 1007
64.1%
ValueCountFrequency (%)
0.2 1
 
0.1%
0.28 1
 
0.1%
0.3 1
 
0.1%
0.34 1
 
0.1%
0.37 1
 
0.1%
0.38 1
 
0.1%
0.39 1
 
0.1%
0.4 1
 
0.1%
0.414 1
 
0.1%
0.5 4
0.3%
ValueCountFrequency (%)
90.5 1
 
0.1%
88.17 2
0.1%
87.42 2
0.1%
87.12 2
0.1%
86.8 2
0.1%
85.54022989 1
 
0.1%
83.93 3
0.2%
82.85 2
0.1%
82.72 3
0.2%
82 1
 
0.1%

NH3-N loss (%)
Real number (ℝ)

Distinct687
Distinct (%)82.1%
Missing733
Missing (%)46.7%
Infinite0
Infinite (%)0.0%
Mean12.072503
Minimum6.0941176 × 10-6
Maximum84.514977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:21.276563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.0941176 × 10-6
5-th percentile0.013294598
Q11.9344706
median10.15
Q318.7
95-th percentile32.4
Maximum84.514977
Range84.514971
Interquartile range (IQR)16.765529

Descriptive statistics

Standard deviation11.536968
Coefficient of variation (CV)0.95564013
Kurtosis4.5748549
Mean12.072503
Median Absolute Deviation (MAD)8.4520458
Skewness1.5104163
Sum10104.685
Variance133.10163
MonotonicityNot monotonic
2024-04-09T15:37:21.426489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 8
 
0.5%
24.9 6
 
0.4%
12.7 6
 
0.4%
9.6 5
 
0.3%
13.3 5
 
0.3%
32.5 4
 
0.3%
9 4
 
0.3%
21.16 4
 
0.3%
18.7 4
 
0.3%
21.2 4
 
0.3%
Other values (677) 787
50.1%
(Missing) 733
46.7%
ValueCountFrequency (%)
6.094117647 × 10-61
0.1%
8.53276788 × 10-61
0.1%
2.890588235 × 10-51
0.1%
5.312462729 × 10-51
0.1%
6.207228026 × 10-51
0.1%
9.369246562 × 10-51
0.1%
0.0001839619463 1
0.1%
0.0002324113647 1
0.1%
0.0002882352941 1
0.1%
0.0003135858353 1
0.1%
ValueCountFrequency (%)
84.51497682 1
0.1%
74.71149316 1
0.1%
74.5876815 1
0.1%
72.7944032 1
0.1%
65 1
0.1%
52.92898646 1
0.1%
50.9 2
0.1%
48.9 1
0.1%
47.9 1
0.1%
44.47 1
0.1%

N2O-N loss (%)
Real number (ℝ)

Distinct414
Distinct (%)72.5%
Missing999
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean1.1660081
Minimum-0.0031818182
Maximum13.05
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.2%
Memory size12.4 KiB
2024-04-09T15:37:21.597801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0031818182
5-th percentile0.00099272727
Q10.036272727
median0.34
Q31.385
95-th percentile5.46
Maximum13.05
Range13.053182
Interquartile range (IQR)1.3487273

Descriptive statistics

Standard deviation2.0478561
Coefficient of variation (CV)1.7562965
Kurtosis9.6611692
Mean1.1660081
Median Absolute Deviation (MAD)0.33669091
Skewness2.9739283
Sum665.79065
Variance4.1937145
MonotonicityNot monotonic
2024-04-09T15:37:21.768117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 9
 
0.6%
1.4 9
 
0.6%
1 9
 
0.6%
1.5 9
 
0.6%
2 8
 
0.5%
1.2 7
 
0.4%
0.6 6
 
0.4%
0.09 4
 
0.3%
0.88 4
 
0.3%
0.2 4
 
0.3%
Other values (404) 502
32.0%
(Missing) 999
63.6%
ValueCountFrequency (%)
-0.003181818182 3
0.2%
0.0001718181818 1
 
0.1%
0.0002093636364 1
 
0.1%
0.0002208181818 1
 
0.1%
0.0002545454545 1
 
0.1%
0.0003181818182 1
 
0.1%
0.000329 1
 
0.1%
0.0003818181818 1
 
0.1%
0.0003964545455 1
 
0.1%
0.0003996363636 1
 
0.1%
ValueCountFrequency (%)
13.05 1
0.1%
12.65063291 1
0.1%
11.22 1
0.1%
11.219 1
0.1%
10.1722 1
0.1%
9.9 1
0.1%
9.3 1
0.1%
9.27 1
0.1%
9.103448276 1
0.1%
8.92 1
0.1%

TC loss (%)
Real number (ℝ)

Distinct306
Distinct (%)69.9%
Missing1132
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean42.426422
Minimum5.1
Maximum92.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:21.931945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile10.417
Q128.1
median44.25
Q354
95-th percentile72.79
Maximum92.58
Range87.48
Interquartile range (IQR)25.9

Descriptive statistics

Standard deviation18.655426
Coefficient of variation (CV)0.43971244
Kurtosis-0.39970277
Mean42.426422
Median Absolute Deviation (MAD)11.79
Skewness0.03461062
Sum18582.773
Variance348.02491
MonotonicityNot monotonic
2024-04-09T15:37:22.040976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 8
 
0.5%
40 5
 
0.3%
44 5
 
0.3%
51 5
 
0.3%
53 4
 
0.3%
55 4
 
0.3%
43 4
 
0.3%
37.8 4
 
0.3%
59.23 4
 
0.3%
32 4
 
0.3%
Other values (296) 391
 
24.9%
(Missing) 1132
72.1%
ValueCountFrequency (%)
5.1 1
0.1%
5.54 1
0.1%
7 1
0.1%
7.04 1
0.1%
7.56 1
0.1%
7.81 1
0.1%
7.9 1
0.1%
8.1 1
0.1%
8.29 1
0.1%
8.3 1
0.1%
ValueCountFrequency (%)
92.58 1
 
0.1%
90.5 1
 
0.1%
88.83 1
 
0.1%
88.75 1
 
0.1%
85.83 1
 
0.1%
83.52 2
0.1%
81.94 3
0.2%
80.91 2
0.1%
80.45 2
0.1%
80 1
 
0.1%

CH4-C loss (%)
Real number (ℝ)

Distinct358
Distinct (%)73.2%
Missing1081
Missing (%)68.9%
Infinite0
Infinite (%)0.0%
Mean2.1898024
Minimum0.000735
Maximum59.34268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:22.197279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.000735
5-th percentile0.0036615
Q10.062867625
median0.32775
Q31.66
95-th percentile7.6697974
Maximum59.34268
Range59.341945
Interquartile range (IQR)1.5971324

Descriptive statistics

Standard deviation6.1210631
Coefficient of variation (CV)2.7952582
Kurtosis41.130019
Mean2.1898024
Median Absolute Deviation (MAD)0.30906
Skewness5.9267407
Sum1070.8134
Variance37.467414
MonotonicityNot monotonic
2024-04-09T15:37:22.341515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 7
 
0.4%
0.05 7
 
0.4%
0.01 7
 
0.4%
0.4 6
 
0.4%
0.8 6
 
0.4%
0.34 6
 
0.4%
0.03 6
 
0.4%
0.6 5
 
0.3%
0.06 5
 
0.3%
0.09 4
 
0.3%
Other values (348) 430
 
27.4%
(Missing) 1081
68.9%
ValueCountFrequency (%)
0.000735 1
0.1%
0.00089616685 1
0.1%
0.0009 1
0.1%
0.0010125 1
0.1%
0.00105 1
0.1%
0.001132061802 2
0.1%
0.001166 1
0.1%
0.0012 1
0.1%
0.00124185 1
0.1%
0.0012666 1
0.1%
ValueCountFrequency (%)
59.34267964 1
0.1%
55.12008912 1
0.1%
44.54663189 1
0.1%
40.60705682 1
0.1%
38.70519299 1
0.1%
35.59202304 1
0.1%
35.15051894 1
0.1%
32.87507472 1
0.1%
27.21476571 1
0.1%
25.2 1
0.1%

CO2-C loss (%)
Real number (ℝ)

Distinct191
Distinct (%)93.6%
Missing1366
Missing (%)87.0%
Infinite0
Infinite (%)0.0%
Mean24.952775
Minimum-0.065865414
Maximum234.43559
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.2%
Memory size12.4 KiB
2024-04-09T15:37:22.504806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.065865414
5-th percentile0.54272727
Q15.2873818
median17.290363
Q336.1
95-th percentile66.594462
Maximum234.43559
Range234.50146
Interquartile range (IQR)30.812618

Descriptive statistics

Standard deviation28.188795
Coefficient of variation (CV)1.1296858
Kurtosis17.171108
Mean24.952775
Median Absolute Deviation (MAD)14.181272
Skewness3.1817408
Sum5090.366
Variance794.60816
MonotonicityNot monotonic
2024-04-09T15:37:22.656950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 2
 
0.1%
31.4 2
 
0.1%
13.9 2
 
0.1%
23.9 2
 
0.1%
0.0004116588369 2
 
0.1%
-0.06586541391 2
 
0.1%
33.3 2
 
0.1%
42.4 2
 
0.1%
45.6 2
 
0.1%
32.1 2
 
0.1%
Other values (181) 184
 
11.7%
(Missing) 1366
87.0%
ValueCountFrequency (%)
-0.06586541391 2
0.1%
-0.03293270695 1
0.1%
0.0004116588369 2
0.1%
0.1237580455 1
0.1%
0.1868503909 1
0.1%
0.1934796534 1
0.1%
0.2727272727 1
0.1%
0.3 1
0.1%
0.5181818182 1
0.1%
0.6818181818 1
0.1%
ValueCountFrequency (%)
234.435591 1
0.1%
154.6684582 1
0.1%
130.9322097 1
0.1%
111.3495988 1
0.1%
109.2624885 1
0.1%
85.08164841 1
0.1%
82.96572199 1
0.1%
74.18915559 1
0.1%
73.43993651 1
0.1%
67.92370809 1
0.1%

Methods
Categorical

Distinct4
Distinct (%)0.5%
Missing711
Missing (%)45.3%
Memory size12.4 KiB
Reactor
576 
Static
227 
Windrow
 
32
Turning
 
24

Length

Max length7
Median length7
Mean length6.7357392
Min length6

Characters and Unicode

Total characters5786
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReactor
2nd rowReactor
3rd rowReactor
4th rowReactor
5th rowReactor

Common Values

ValueCountFrequency (%)
Reactor 576
36.7%
Static 227
 
14.5%
Windrow 32
 
2.0%
Turning 24
 
1.5%
(Missing) 711
45.3%

Length

2024-04-09T15:37:22.800689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T15:37:22.934366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
reactor 576
67.1%
static 227
 
26.4%
windrow 32
 
3.7%
turning 24
 
2.8%

Most occurring characters

ValueCountFrequency (%)
t 1030
17.8%
a 803
13.9%
c 803
13.9%
r 632
10.9%
o 608
10.5%
R 576
10.0%
e 576
10.0%
i 283
 
4.9%
S 227
 
3.9%
n 80
 
1.4%
Other values (6) 168
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4927
85.2%
Uppercase Letter 859
 
14.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1030
20.9%
a 803
16.3%
c 803
16.3%
r 632
12.8%
o 608
12.3%
e 576
11.7%
i 283
 
5.7%
n 80
 
1.6%
d 32
 
0.6%
w 32
 
0.6%
Other values (2) 48
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
R 576
67.1%
S 227
 
26.4%
W 32
 
3.7%
T 24
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 5786
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1030
17.8%
a 803
13.9%
c 803
13.9%
r 632
10.9%
o 608
10.5%
R 576
10.0%
e 576
10.0%
i 283
 
4.9%
S 227
 
3.9%
n 80
 
1.4%
Other values (6) 168
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1030
17.8%
a 803
13.9%
c 803
13.9%
r 632
10.9%
o 608
10.5%
R 576
10.0%
e 576
10.0%
i 283
 
4.9%
S 227
 
3.9%
n 80
 
1.4%
Other values (6) 168
 
2.9%

Time Period
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing905
Missing (%)57.6%
Memory size12.4 KiB

堆体大小(m3)
Real number (ℝ)

Distinct50
Distinct (%)33.8%
Missing1422
Missing (%)90.6%
Infinite0
Infinite (%)0.0%
Mean4.3778926
Minimum0.0035
Maximum202.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:23.075029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0035
5-th percentile0.01
Q10.06
median0.3235
Q31.252
95-th percentile10
Maximum202.5
Range202.4965
Interquartile range (IQR)1.192

Descriptive statistics

Standard deviation21.089674
Coefficient of variation (CV)4.817312
Kurtosis64.742818
Mean4.3778926
Median Absolute Deviation (MAD)0.3135
Skewness7.7411719
Sum647.9281
Variance444.77436
MonotonicityNot monotonic
2024-04-09T15:37:23.200438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06 24
 
1.5%
0.03 10
 
0.6%
1.37 10
 
0.6%
0.01 9
 
0.6%
1.2 8
 
0.5%
0.1 7
 
0.4%
1 6
 
0.4%
1.12 6
 
0.4%
10 5
 
0.3%
0.2 4
 
0.3%
Other values (40) 59
 
3.8%
(Missing) 1422
90.6%
ValueCountFrequency (%)
0.0035 1
 
0.1%
0.01 9
 
0.6%
0.026 1
 
0.1%
0.03 10
0.6%
0.0318 1
 
0.1%
0.032 1
 
0.1%
0.0488 1
 
0.1%
0.049 1
 
0.1%
0.06 24
1.5%
0.08 1
 
0.1%
ValueCountFrequency (%)
202.5 1
 
0.1%
140 1
 
0.1%
63 1
 
0.1%
36 2
 
0.1%
11.25 2
 
0.1%
10 5
0.3%
9.75 2
 
0.1%
4.5 1
 
0.1%
2 1
 
0.1%
1.9 3
0.2%

初始容重(kgL)
Real number (ℝ)

Distinct74
Distinct (%)54.0%
Missing1433
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean0.4659708
Minimum0.05
Maximum1.4285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:23.276748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.1
Q10.3
median0.444
Q30.616
95-th percentile0.926
Maximum1.4285
Range1.3785
Interquartile range (IQR)0.316

Descriptive statistics

Standard deviation0.27278441
Coefficient of variation (CV)0.58541095
Kurtosis0.63033
Mean0.4659708
Median Absolute Deviation (MAD)0.1565
Skewness0.80200289
Sum63.838
Variance0.074411334
MonotonicityNot monotonic
2024-04-09T15:37:23.347521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 12
 
0.8%
0.305 8
 
0.5%
0.4 6
 
0.4%
0.123 6
 
0.4%
0.3 6
 
0.4%
0.1 5
 
0.3%
0.492 4
 
0.3%
0.667 4
 
0.3%
0.1715 4
 
0.3%
0.616 3
 
0.2%
Other values (64) 79
 
5.0%
(Missing) 1433
91.3%
ValueCountFrequency (%)
0.05 2
 
0.1%
0.073 2
 
0.1%
0.099 2
 
0.1%
0.1 5
0.3%
0.11 1
 
0.1%
0.123 6
0.4%
0.14 2
 
0.1%
0.1715 4
0.3%
0.21 1
 
0.1%
0.229 1
 
0.1%
ValueCountFrequency (%)
1.4285 1
0.1%
1.274 1
0.1%
1.105 1
0.1%
1.061 2
0.1%
1.033 1
0.1%
0.95 1
0.1%
0.92 1
0.1%
0.891 1
0.1%
0.89 1
0.1%
0.886 1
0.1%

Ventilation
Categorical

Distinct3
Distinct (%)0.7%
Missing1156
Missing (%)73.6%
Memory size12.4 KiB
force aeration+turning
263 
turning
94 
force aeration
57 

Length

Max length22
Median length22
Mean length17.492754
Min length7

Characters and Unicode

Total characters7242
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowforce aeration+turning
2nd rowforce aeration+turning
3rd rowforce aeration+turning
4th rowforce aeration+turning
5th rowforce aeration+turning

Common Values

ValueCountFrequency (%)
force aeration+turning 263
 
16.8%
turning 94
 
6.0%
force aeration 57
 
3.6%
(Missing) 1156
73.6%

Length

2024-04-09T15:37:23.424144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T15:37:23.491898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
force 320
43.6%
aeration+turning 263
35.8%
turning 94
 
12.8%
aeration 57
 
7.8%

Most occurring characters

ValueCountFrequency (%)
n 1034
14.3%
r 997
13.8%
t 677
9.3%
i 677
9.3%
o 640
8.8%
e 640
8.8%
a 640
8.8%
u 357
 
4.9%
g 357
 
4.9%
f 320
 
4.4%
Other values (3) 903
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6659
91.9%
Space Separator 320
 
4.4%
Math Symbol 263
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1034
15.5%
r 997
15.0%
t 677
10.2%
i 677
10.2%
o 640
9.6%
e 640
9.6%
a 640
9.6%
u 357
 
5.4%
g 357
 
5.4%
f 320
 
4.8%
Space Separator
ValueCountFrequency (%)
320
100.0%
Math Symbol
ValueCountFrequency (%)
+ 263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6659
91.9%
Common 583
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1034
15.5%
r 997
15.0%
t 677
10.2%
i 677
10.2%
o 640
9.6%
e 640
9.6%
a 640
9.6%
u 357
 
5.4%
g 357
 
5.4%
f 320
 
4.8%
Common
ValueCountFrequency (%)
320
54.9%
+ 263
45.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1034
14.3%
r 997
13.8%
t 677
9.3%
i 677
9.3%
o 640
8.8%
e 640
8.8%
a 640
8.8%
u 357
 
4.9%
g 357
 
4.9%
f 320
 
4.4%
Other values (3) 903
12.5%

通风速率(L min-1 kg DW)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1287
Missing (%)82.0%
Memory size12.4 KiB

Peak Temperature (℃)
Real number (ℝ)

Distinct95
Distinct (%)76.0%
Missing1445
Missing (%)92.0%
Infinite0
Infinite (%)0.0%
Mean57.37624
Minimum29.55
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:23.558124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum29.55
5-th percentile37.64
Q151.6
median57.9
Q365.5
95-th percentile71.86
Maximum79
Range49.45
Interquartile range (IQR)13.9

Descriptive statistics

Standard deviation10.498781
Coefficient of variation (CV)0.18298133
Kurtosis-0.23279112
Mean57.37624
Median Absolute Deviation (MAD)7.1
Skewness-0.54517236
Sum7172.03
Variance110.22439
MonotonicityNot monotonic
2024-04-09T15:37:23.627365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 4
 
0.3%
54 4
 
0.3%
51 3
 
0.2%
56 3
 
0.2%
64.4 3
 
0.2%
56.5 3
 
0.2%
50 3
 
0.2%
53.5 3
 
0.2%
68 2
 
0.1%
35 2
 
0.1%
Other values (85) 95
 
6.1%
(Missing) 1445
92.0%
ValueCountFrequency (%)
29.55 1
0.1%
33 1
0.1%
33.4 1
0.1%
35 2
0.1%
37.2 1
0.1%
37.6 1
0.1%
37.8 1
0.1%
38.1 1
0.1%
39 1
0.1%
39.1 1
0.1%
ValueCountFrequency (%)
79 1
0.1%
75 1
0.1%
74 1
0.1%
73 1
0.1%
72.7 1
0.1%
72.2 1
0.1%
72 1
0.1%
71.3 1
0.1%
71 1
0.1%
70.77 1
0.1%
Distinct59
Distinct (%)80.8%
Missing1497
Missing (%)95.4%
Infinite0
Infinite (%)0.0%
Mean38.027397
Minimum-13.23
Maximum65
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size12.4 KiB
2024-04-09T15:37:23.697355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-13.23
5-th percentile13.93
Q124.4
median37.74
Q359.39
95-th percentile65
Maximum65
Range78.23
Interquartile range (IQR)34.99

Descriptive statistics

Standard deviation18.382047
Coefficient of variation (CV)0.48338958
Kurtosis-0.63151884
Mean38.027397
Median Absolute Deviation (MAD)14.32
Skewness-0.073591761
Sum2776
Variance337.89967
MonotonicityNot monotonic
2024-04-09T15:37:23.761754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 9
 
0.6%
32 4
 
0.3%
41.12 2
 
0.1%
60 2
 
0.1%
60.08 2
 
0.1%
38.98 1
 
0.1%
35.76 1
 
0.1%
40.83 1
 
0.1%
14.83 1
 
0.1%
40.72 1
 
0.1%
Other values (49) 49
 
3.1%
(Missing) 1497
95.4%
ValueCountFrequency (%)
-13.23 1
0.1%
5 1
0.1%
11 1
0.1%
12.58 1
0.1%
14.83 1
0.1%
15 1
0.1%
15.08 1
0.1%
16.01 1
0.1%
16.22 1
0.1%
17.1 1
0.1%
ValueCountFrequency (%)
65 9
0.6%
62.42 1
 
0.1%
61 1
 
0.1%
60.4 1
 
0.1%
60.19 1
 
0.1%
60.08 2
 
0.1%
60.04 1
 
0.1%
60 2
 
0.1%
59.39 1
 
0.1%
55 1
 
0.1%
Distinct188
Distinct (%)95.4%
Missing1373
Missing (%)87.5%
Infinite0
Infinite (%)0.0%
Mean0.6483094
Minimum0.017094017
Maximum3.2338308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:23.831269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.017094017
5-th percentile0.14155844
Q10.40464345
median0.66666667
Q30.8277512
95-th percentile1.0313626
Maximum3.2338308
Range3.2167368
Interquartile range (IQR)0.42310775

Descriptive statistics

Standard deviation0.36662799
Coefficient of variation (CV)0.56551392
Kurtosis14.604485
Mean0.6483094
Median Absolute Deviation (MAD)0.19733333
Skewness2.4893328
Sum127.71695
Variance0.13441608
MonotonicityNot monotonic
2024-04-09T15:37:23.895113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.68173258 2
 
0.1%
0.8757062147 2
 
0.1%
1 2
 
0.1%
0.2781 2
 
0.1%
0.87 2
 
0.1%
0.1347 2
 
0.1%
0.3594 2
 
0.1%
0.7380952381 2
 
0.1%
0.5013333333 2
 
0.1%
0.4 1
 
0.1%
Other values (178) 178
 
11.3%
(Missing) 1373
87.5%
ValueCountFrequency (%)
0.01709401709 1
0.1%
0.01769230769 1
0.1%
0.0235042735 1
0.1%
0.02857142857 1
0.1%
0.03846153846 1
0.1%
0.0438 1
0.1%
0.1108 1
0.1%
0.1347 2
0.1%
0.1363636364 1
0.1%
0.1428571429 1
0.1%
ValueCountFrequency (%)
3.233830846 1
0.1%
2.304250049 1
0.1%
2.154228856 1
0.1%
1.555070423 1
0.1%
1.365853659 1
0.1%
1.170731707 1
0.1%
1.153609831 1
0.1%
1.088801331 1
0.1%
1.067588326 1
0.1%
1.039987504 1
0.1%
Distinct194
Distinct (%)96.5%
Missing1369
Missing (%)87.2%
Infinite0
Infinite (%)0.0%
Mean0.73089207
Minimum0.017857143
Maximum6.1666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:23.970699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.017857143
5-th percentile0.14285714
Q10.4137931
median0.63793236
Q30.79548989
95-th percentile1.3040528
Maximum6.1666667
Range6.1488095
Interquartile range (IQR)0.38169679

Descriptive statistics

Standard deviation0.75477181
Coefficient of variation (CV)1.032672
Kurtosis31.529311
Mean0.73089207
Median Absolute Deviation (MAD)0.18869541
Skewness5.1865537
Sum146.90931
Variance0.56968048
MonotonicityNot monotonic
2024-04-09T15:37:24.041405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4107142857 2
 
0.1%
1.078947368 2
 
0.1%
0.7 2
 
0.1%
0.4461538462 2
 
0.1%
0.7777777778 2
 
0.1%
0.74 2
 
0.1%
0.1078431373 2
 
0.1%
0.623 1
 
0.1%
0.347826087 1
 
0.1%
0.8975903614 1
 
0.1%
Other values (184) 184
 
11.7%
(Missing) 1369
87.2%
ValueCountFrequency (%)
0.01785714286 1
0.1%
0.01960784314 1
0.1%
0.03921568627 1
0.1%
0.05882352941 1
0.1%
0.0625 1
0.1%
0.09523809524 1
0.1%
0.1 1
0.1%
0.1078431373 2
0.1%
0.12 1
0.1%
0.1428571429 1
0.1%
ValueCountFrequency (%)
6.166666667 1
0.1%
5.633333333 1
0.1%
5.483333333 1
0.1%
4.5 1
0.1%
2.398311049 1
0.1%
1.758506616 1
0.1%
1.686206897 1
0.1%
1.585635359 1
0.1%
1.547169811 1
0.1%
1.533333333 1
0.1%
Distinct91
Distinct (%)91.9%
Missing1471
Missing (%)93.7%
Infinite0
Infinite (%)0.0%
Mean0.84587617
Minimum0.008
Maximum5.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:24.111513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.027588556
Q10.36683488
median0.79489796
Q31.0512957
95-th percentile1.7686862
Maximum5.96
Range5.952
Interquartile range (IQR)0.68446085

Descriptive statistics

Standard deviation0.72974898
Coefficient of variation (CV)0.86271372
Kurtosis24.040143
Mean0.84587617
Median Absolute Deviation (MAD)0.3227491
Skewness3.7433216
Sum83.741741
Variance0.53253357
MonotonicityNot monotonic
2024-04-09T15:37:24.183059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9333333333 4
 
0.3%
0.8 4
 
0.3%
0.76 2
 
0.1%
0.9 2
 
0.1%
1.382352941 1
 
0.1%
0.534 1
 
0.1%
0.02452316076 1
 
0.1%
0.9804878049 1
 
0.1%
0.7196041763 1
 
0.1%
0.164 1
 
0.1%
Other values (81) 81
 
5.2%
(Missing) 1471
93.7%
ValueCountFrequency (%)
0.008 1
0.1%
0.01219346049 1
0.1%
0.02 1
0.1%
0.02077656676 1
0.1%
0.02452316076 1
0.1%
0.02792915531 1
0.1%
0.036 1
0.1%
0.05704099822 1
0.1%
0.147 1
0.1%
0.1474 1
0.1%
ValueCountFrequency (%)
5.96 1
0.1%
2.566334992 1
0.1%
2.345771144 1
0.1%
1.98630137 1
0.1%
1.782752902 1
0.1%
1.767123288 1
0.1%
1.727197347 1
0.1%
1.667 1
0.1%
1.583333333 1
0.1%
1.501 1
0.1%
Distinct65
Distinct (%)94.2%
Missing1501
Missing (%)95.6%
Infinite0
Infinite (%)0.0%
Mean0.55437993
Minimum0.0073751257
Maximum2.6976744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-09T15:37:24.255828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0073751257
5-th percentile0.018095946
Q10.18
median0.5277
Q30.78145606
95-th percentile1.1092437
Maximum2.6976744
Range2.6902993
Interquartile range (IQR)0.60145606

Descriptive statistics

Standard deviation0.49014141
Coefficient of variation (CV)0.88412546
Kurtosis6.4941137
Mean0.55437993
Median Absolute Deviation (MAD)0.33688333
Skewness1.9610525
Sum38.252215
Variance0.2402386
MonotonicityNot monotonic
2024-04-09T15:37:24.329749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2727272727 2
 
0.1%
1.058823529 2
 
0.1%
0.12 2
 
0.1%
0.7814560596 2
 
0.1%
0.6767676768 1
 
0.1%
1.047619048 1
 
0.1%
1.153846154 1
 
0.1%
0.225 1
 
0.1%
0.5588235294 1
 
0.1%
0.1473438409 1
 
0.1%
Other values (55) 55
 
3.5%
(Missing) 1501
95.6%
ValueCountFrequency (%)
0.007375125712 1
0.1%
0.008380824673 1
0.1%
0.01005698961 1
0.1%
0.01106268857 1
0.1%
0.02864583333 1
0.1%
0.03 1
0.1%
0.033203125 1
0.1%
0.047 1
0.1%
0.05 1
0.1%
0.1155 1
0.1%
ValueCountFrequency (%)
2.697674419 1
0.1%
2.406417112 1
0.1%
1.153846154 1
0.1%
1.142857143 1
0.1%
1.058823529 2
0.1%
1.047619048 1
0.1%
1.045859305 1
0.1%
1 1
0.1%
0.9433333333 1
0.1%
0.9117647059 1
0.1%

Scale
Categorical

Distinct6
Distinct (%)0.6%
Missing581
Missing (%)37.0%
Memory size12.4 KiB
lab
526 
pilot
169 
lab
154 
Lab
79 
Field
58 

Length

Max length11
Median length3
Mean length3.6390293
Min length3

Characters and Unicode

Total characters3599
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlab
2nd rowlab
3rd rowlab
4th rowlab
5th rowlab

Common Values

ValueCountFrequency (%)
lab 526
33.5%
pilot 169
 
10.8%
lab 154
 
9.8%
Lab 79
 
5.0%
Field 58
 
3.7%
Large scale 3
 
0.2%
(Missing) 581
37.0%

Length

2024-04-09T15:37:24.397561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T15:37:24.462473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
lab 759
76.5%
pilot 169
 
17.0%
field 58
 
5.8%
large 3
 
0.3%
scale 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
l 910
25.3%
a 765
21.3%
b 759
21.1%
i 227
 
6.3%
p 169
 
4.7%
o 169
 
4.7%
t 169
 
4.7%
157
 
4.4%
L 82
 
2.3%
e 64
 
1.8%
Other values (6) 128
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3302
91.7%
Space Separator 157
 
4.4%
Uppercase Letter 140
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 910
27.6%
a 765
23.2%
b 759
23.0%
i 227
 
6.9%
p 169
 
5.1%
o 169
 
5.1%
t 169
 
5.1%
e 64
 
1.9%
d 58
 
1.8%
r 3
 
0.1%
Other values (3) 9
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
L 82
58.6%
F 58
41.4%
Space Separator
ValueCountFrequency (%)
157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3442
95.6%
Common 157
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 910
26.4%
a 765
22.2%
b 759
22.1%
i 227
 
6.6%
p 169
 
4.9%
o 169
 
4.9%
t 169
 
4.9%
L 82
 
2.4%
e 64
 
1.9%
F 58
 
1.7%
Other values (5) 70
 
2.0%
Common
ValueCountFrequency (%)
157
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 910
25.3%
a 765
21.3%
b 759
21.1%
i 227
 
6.3%
p 169
 
4.7%
o 169
 
4.7%
t 169
 
4.7%
157
 
4.4%
L 82
 
2.3%
e 64
 
1.8%
Other values (6) 128
 
3.6%

Interactions

2024-04-09T15:37:11.029465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:41.190317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:42.604745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:43.847325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:45.223153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:46.567743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:47.839051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:49.214696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:50.520511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:51.698275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:53.251696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:55.225876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:57.373576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:59.101476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:00.630373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:01.976504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:03.274525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:04.720337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:06.816221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:08.128033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:09.461368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:11.194381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-04-09T15:36:43.788759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:45.167402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:46.518685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:47.786505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:49.159388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:50.459661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:51.642276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:53.185142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:55.102507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:57.293911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:36:59.041282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:00.567394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:01.907250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:03.217309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:04.638141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:06.752401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:08.063987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:09.398834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-09T15:37:10.916247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Missing values

2024-04-09T15:37:13.201857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-09T15:37:13.804177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-09T15:37:14.800361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idtitleauthorlocationJournalmaterial_0material_1ExcipientsAdditive SpeciesAdditive_1Additive_2Application Rate (%)initial moisture content(%)initial pHinitial TN(%)initial TC(%)initial CN(%)TN loss (%)NH3-N loss (%)N2O-N loss (%)TC loss (%)CH4-C loss (%)CO2-C loss (%)MethodsTime Period堆体大小(m3)初始容重(kgL)Ventilation通风速率(L min-1 kg DW)Peak Temperature (℃)Average Temperature (℃)the ratio of treatment to control-TNthe ratio of treatment to control-NH3the ratio of treatment to control-N2Othe ratio of treatment to control-CH4Scale
01NaNLimei Ren et al.,2010Chinawaste managementManureSwine manureNaNNaNNaNNaNNaN65.07.606642.6639.9015.00000035.028.0NaNNaNNaNNaNNaNNaNNaNNaNforce aeration+turningNaNNaNNaNNaNNaNNaNNaNlab
11NaNLimei Ren et al.,2010Chinawaste managementManureSwine manureNaNChemicalPO43- and Mg2+ saltsNaN15.4NaN7.41084NaNNaNNaN12.09.0NaNNaNNaNNaNNaNNaNNaNNaNforce aeration+turningNaNNaNNaN0.3428570.321429NaNNaNlab
21NaNLimei Ren et al.,2010Chinawaste managementManureSwine manureNaNChemicalPO43- and Mg2+ saltsNaN18.7NaN6.61538NaNNaNNaN5.04.0NaNNaNNaNNaNNaNNaNNaNNaNforce aeration+turningNaNNaNNaN0.1428570.142857NaNNaNlab
31NaNLimei Ren et al.,2010Chinawaste managementManureSwine manureNaNChemicalPO43- and Mg2+ saltsNaN17.1NaN6.40734NaNNaNNaN1.00.5NaNNaNNaNNaNNaNNaNNaNNaNforce aeration+turningNaNNaNNaN0.0285710.017857NaNNaNlab
42NaNKhalil Tubail et al.,2008UScompost science & utilizationManureCow manureNaNNaNNaNNaNNaNNaN7.520001.3147.1636.00000016.0NaNNaNNaNNaNNaNNaNNaNNaNNaNforce aeration+turningNaNNaNNaNNaNNaNNaNNaNlab
52NaNKhalil Tubail et al.,2008UScompost science & utilizationManureCow manureNaNPhysicalgypsumNaNNaNNaN7.58000NaNNaNNaN6.1NaNNaNNaNNaNNaNNaNNaNNaNNaNforce aeration+turningNaNNaNNaN0.381250NaNNaNNaNlab
62NaNKhalil Tubail et al.,2008UScompost science & utilizationSewage sludgeNaNNaNNaNNaNNaNNaNNaN7.700002.8554.1519.00000025.1NaNNaNNaNNaNNaNNaNNaNNaNNaNforce aeration+turningNaNNaNNaNNaNNaNNaNNaNlab
72NaNKhalil Tubail et al.,2008UScompost science & utilizationSewage sludgeNaNNaNPhysicalgypsumNaNNaNNaN7.40000NaNNaNNaN11.4NaNNaNNaNNaNNaNNaNNaNNaNNaNforce aeration+turningNaNNaNNaN0.454183NaNNaNNaNlab
83NaNJishao Jiang et al.,2014Chinawaste managementManureSwine manureNaNNaNNaNNaNNaN65.06.498422.4632.5013.21138258.03.0NaNNaNNaNNaNNaNNaNNaNNaNforce aeration+turningNaNNaNNaNNaNNaNNaNNaNlab
93NaNJishao Jiang et al.,2014Chinawaste managementManureSwine manureNaNPhysicalbentoniteNaNNaNNaN7.200002.7530.1310.95636450.34.6NaNNaNNaNNaNNaNNaNNaNNaNforce aeration+turningNaNNaNNaN0.8672411.533333NaNNaNlab
idtitleauthorlocationJournalmaterial_0material_1ExcipientsAdditive SpeciesAdditive_1Additive_2Application Rate (%)initial moisture content(%)initial pHinitial TN(%)initial TC(%)initial CN(%)TN loss (%)NH3-N loss (%)N2O-N loss (%)TC loss (%)CH4-C loss (%)CO2-C loss (%)MethodsTime Period堆体大小(m3)初始容重(kgL)Ventilation通风速率(L min-1 kg DW)Peak Temperature (℃)Average Temperature (℃)the ratio of treatment to control-TNthe ratio of treatment to control-NH3the ratio of treatment to control-N2Othe ratio of treatment to control-CH4Scale
1560435碳源调控对污泥堆肥过程氮素损失的影响及其作用机制NaN中国/黑龙江/哈尔滨NaNSewage sludgeNaN蔗糖NaNNaNNaNNaN63.137.07NaNNaNNaNNaN29.842400NaNNaNNaNNaNNaNNaNNaNNaNNaN0.2NaNNaNNaNNaNNaNNaNNaN
1561435碳源调控对污泥堆肥过程氮素损失的影响及其作用机制NaN中国/黑龙江/哈尔滨NaNSewage sludgeNaN蔗糖+秸秆NaNNaNNaNNaN57.526.55NaNNaNNaNNaN9.144800NaNNaNNaNNaNNaNNaNNaNNaNNaN0.2NaNNaNNaNNaNNaNNaNNaN
1562435碳源调控对污泥堆肥过程氮素损失的影响及其作用机制NaN中国/黑龙江/哈尔滨NaNSewage sludgeNaN蔗糖+秸秆NaNNaNNaNNaN61.077.29NaNNaNNaNNaN7.915800NaNNaNNaNNaNNaNNaNNaNNaNNaN0.2NaNNaNNaNNaNNaNNaNNaN
1563435碳源调控对污泥堆肥过程氮素损失的影响及其作用机制NaN中国/黑龙江/哈尔滨NaNSewage sludgeNaN蔗糖+秸秆NaNNaNNaNNaN60.617.39NaNNaNNaNNaN5.054400NaNNaNNaNNaNNaNNaNNaNNaNNaN0.2NaNNaNNaNNaNNaNNaNNaN
1564435碳源调控对污泥堆肥过程氮素损失的影响及其作用机制NaN中国/黑龙江/哈尔滨NaNSewage sludgeNaN蔗糖+秸秆NaNNaNNaNNaN59.857.29NaNNaNNaNNaN14.817600NaNNaNNaNNaNNaNNaNNaNNaNNaN0.2NaNNaNNaNNaNNaNNaNNaN
1565435碳源调控对污泥堆肥过程氮素损失的影响及其作用机制NaN中国/黑龙江/哈尔滨NaNSewage sludgeNaN蔗糖+秸秆NaNNaNNaNNaN60.997.30NaNNaNNaNNaN23.287500NaNNaNNaNNaNNaNNaNNaNNaNNaN0.2NaNNaNNaNNaNNaNNaNNaN
1566436原位固氮剂在污泥堆肥过程中保氮机制NaN中国/河南/新乡NaNSewage sludgeNaN木屑NaNNaNNaNNaNNaN7.44NaNNaNNaNNaN2.691121NaNNaNNaNNaNNaNNaNNaNNaNNaN80mL/minNaNNaNNaNNaNNaNNaNNaN
1567436原位固氮剂在污泥堆肥过程中保氮机制NaN中国/河南/新乡NaNSewage sludgeNaN木屑+MgSO4NaNNaNNaNNaNNaN7.20NaNNaNNaNNaN1.593054NaNNaNNaNNaNNaNNaNNaNNaNNaN80mL/minNaNNaNNaNNaNNaNNaNNaN
1568436原位固氮剂在污泥堆肥过程中保氮机制NaN中国/河南/新乡NaNSewage sludgeNaN木屑+KPMNaNNaNNaNNaNNaN7.09NaNNaNNaNNaN1.869247NaNNaNNaNNaNNaNNaNNaNNaNNaN80mL/minNaNNaNNaNNaNNaNNaNNaN
1569436原位固氮剂在污泥堆肥过程中保氮机制NaN中国/河南/新乡NaNSewage sludgeNaN木屑+KPNaNNaNNaNNaNNaN7.44NaNNaNNaNNaN3.858471NaNNaNNaNNaNNaNNaNNaNNaNNaN80mL/minNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

idtitleauthorlocationJournalmaterial_0material_1ExcipientsAdditive SpeciesAdditive_1Additive_2Application Rate (%)initial moisture content(%)initial pHinitial TN(%)initial TC(%)initial CN(%)TN loss (%)NH3-N loss (%)N2O-N loss (%)TC loss (%)CH4-C loss (%)CO2-C loss (%)Methods堆体大小(m3)初始容重(kgL)VentilationPeak Temperature (℃)Average Temperature (℃)the ratio of treatment to control-TNthe ratio of treatment to control-NH3the ratio of treatment to control-N2Othe ratio of treatment to control-CH4Scale# duplicates
37173Nitrogen loss in chicken litter compost as affected by carbon to nitrogen ratio and turning frequencyOgunwande et al., 2008NaNBioresource TechnologyManurePoultry manureSawdustNaNNaNNaNNaNNaNNaNNaNNaN20.082.72NaNNaN72.20NaNNaNStaticNaNNaNNaNNaNNaNNaNNaNNaNNaNpilot3
40173Nitrogen loss in chicken litter compost as affected by carbon to nitrogen ratio and turning frequencyOgunwande et al., 2008NaNBioresource TechnologyManurePoultry manureSawdustNaNNaNNaNNaNNaNNaNNaNNaN25.070.73NaNNaN72.40NaNNaNStaticNaNNaNNaNNaNNaNNaNNaNNaNNaNpilot3
44173Nitrogen loss in chicken litter compost as affected by carbon to nitrogen ratio and turning frequencyOgunwande et al., 2008NaNBioresource TechnologyManurePoultry manureSawdustNaNNaNNaNNaNNaNNaNNaNNaN30.083.93NaNNaN81.94NaNNaNStaticNaNNaNNaNNaNNaNNaNNaNNaNNaNpilot3
028NaNLeimei Ren et al.,2009ChinaJournal of Agro-Environment Science (in Chinese)ManurePoultry manureNaNChemicalPO43- and Mg2+ saltsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNturningNaNNaN0.1347NaNNaNNaNlab2
128NaNLeimei Ren et al.,2009ChinaJournal of Agro-Environment Science (in Chinese)ManurePoultry manureNaNChemicalPO43- and Mg2+ saltsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNturningNaNNaN0.2781NaNNaNNaNlab2
228NaNLeimei Ren et al.,2009ChinaJournal of Agro-Environment Science (in Chinese)ManurePoultry manureNaNChemicalPO43- and Mg2+ saltsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNturningNaNNaN0.3594NaNNaNNaNlab2
3119Biochar combined with gypsum reduces both nitrogen and carbon losses during agricultural waste composting and enhances overall compost quality by regulating microbial activities and functionsQu et al., 2020NaNBioresource TechnologyManurePoultry manurePlant strawPhysicalBiocharNaN5.060.07.481.6348.930.016.99NaNNaN40.50NaNNaNReactorNaNNaNNaNNaNNaNNaNNaNNaNNaNlab2
4119Biochar combined with gypsum reduces both nitrogen and carbon losses during agricultural waste composting and enhances overall compost quality by regulating microbial activities and functionsQu et al., 2020NaNBioresource TechnologyManurePoultry manurePlant strawPhysicalBiocharNaN5.060.07.691.7844.525.018.35NaNNaN30.60NaNNaNReactorNaNNaNNaNNaNNaNNaNNaNNaNNaNlab2
5119Biochar combined with gypsum reduces both nitrogen and carbon losses during agricultural waste composting and enhances overall compost quality by regulating microbial activities and functionsQu et al., 2020NaNBioresource TechnologyManurePoultry manurePlant strawPhysicalBiocharNaN5.060.07.902.0240.420.028.54NaNNaN37.80NaNNaNReactorNaNNaNNaNNaNNaNNaNNaNNaNNaNlab2
6119Biochar combined with gypsum reduces both nitrogen and carbon losses during agricultural waste composting and enhances overall compost quality by regulating microbial activities and functionsQu et al., 2020NaNBioresource TechnologyManurePoultry manurePlant strawPhysicalGypsumNaN5.060.07.131.7051.030.08.15NaNNaN37.80NaNNaNReactorNaNNaNNaNNaNNaNNaNNaNNaNNaNlab2